An effective Load shedding technique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system

Conteh, Foday; Tobaru, Shota; Lotfy, Mohammed Elsayed; Yona, Atsushi; Senjyu, Tomonobu

doi:10.3934/energy.2017.5.814

Cited by 10 publications

(10 citation statements)

References 13 publications

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“…The ANN-based load curtailment method can optimize the amount of load shedding in order to stabilize the power system [16]. A load curtailment based on a combination between ANN and neuro-fuzzy was proposed in [15]. The results show that the ANN can compute the total amount of active power that needs to be shed but it does not determine the number of loads or the distributed load shedding power that need to be shed in each demand load.…”

Section: Imentioning

confidence: 99%

“…In fact, the GA can help in making the calculation speed for load shedding scenario robust, however the limitation of this algorithm is that it cannot help in optimal distributed load shedding for each demand load bus. The efficiency of PSO has been applied in load shedding techniques in [15,18]. The studies show that PSO can forecast the frequency droop and optimize the amount of the required load demand.…”

Section: Imentioning

confidence: 99%

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Load Shedding in Microgrids with Consideration of Voltage Quality Improvement

Phung

2021

Eng. Technol. Appl. Sci. Res.

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Microgrids have become more and more popular their usefulness as a renewable energy resource has been recognized. The core ability and promise of microgrids is addressing the environmental concerns due to climate change that have been growing during recent years. The innovation of microgrids is that they are designed to operate either in island mode or interconnected with the main grid system. However, when the microgrid operates in islanded mode, faults may occur which can cause system collapse or even blackout. Load curtailment schemes can be utilized to decrease the quantity of associated load to a level that can be securely supported by accessible generation in isolated mode. The main goal of this research is to evaluate the optimal amount of shedding power considering sustainable power sources, with the help of primary and secondary adjustments of the generator to restore the frequency to the allowed range. Particle Swarm Optimization algorithm is applied in this paper to determine the distributed shedding power on each demand load bus which can improve the voltage quality of the isolated microgrid system. The effectiveness of the proposed method is demonstrated through the simulation of IEEE 16- bus microgrid.

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Section: Imentioning

confidence: 99%

Section: Imentioning

confidence: 99%

Load Shedding in Microgrids with Consideration of Voltage Quality Improvement

Phung

2021

Eng. Technol. Appl. Sci. Res.

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“…Various methods are presently being utilized for the performance optimization of the hydropower plant. Conteh et al [5] developed an optimal load-shedding technique capable of shedding the required load. It utilizes the backpropagation artificial neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Author(s) Method (s) Objective (s) Findings Conteh et al [5] ANFIS Optimal load shedding that can shed the required amount of load for grid stability…”

Section: Literature Reviewmentioning

confidence: 99%

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Application of Artificial Intelligence for the Optimization of Hydropower Energy Generation

Kumar¹,

Saini²

2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Hydropower is one of the most promising sources of renewable energy. However, a substantial initial investment requires for the construction of large civil structures. Feasibility study, detailed project report preparation, construction planning, and timely execution of work are the important activities of a hydropower plant. Energy generation in hydropower plants are mainly depends on discharge and head. Therefore, an accurate estimation of discharge and head is important to decide the plant capacity. Erosion, cavitation, and operation & maintenance are the key challenges in hydropower energy generation. Artificial Intelligence (AI) has become popular, which can be utilized for site selection, parameters assessment, and operation & maintenance optimization. In this paper, a literature review on applications of AI in hydropower has been presented, and an attempt has also been made to identify the future potential areas of hydropower plants.

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